Equipment For Manufacturing Semiconductor Device And Seasoning Process Method Of The Same

ABSTRACT

Disclosed is an apparatus for processing a semiconductor and a method for generating a seasoning process of a reaction chamber. The method may include generating plasma in the reaction chamber using a production process recipe, obtaining at least one reference measurement value related to a byproduct of the generated plasma, performing a plurality of seasoning tests on the chamber to obtain a plurality of test results, generating an empirical model by forming at least one relational expression correlating variables manipulated during the performing of the plurality of seasoning tests to the plurality of test results, and estimating a seasoning process by using the at least one relational expression to estimate at least one estimated calculation value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This U.S. non-provisional patent application claims priority under 35U.S.C. §119 to Korean Patent Application No. 10-2010-0049284, filed onMay 26, 2010, in the Korean Intellectual Property Office (KIPO), theentire contents of which are hereby incorporated by reference.

BACKGROUND

1. Field

The present disclosure herein relates to equipment for manufacturing asemiconductor device and a method for generating a seasoning process ofthe same, and more particularly, to equipment for manufacturing asemiconductor device where a seasoning process is performed aftercleaning a plasma reaction chamber and a method for generating theseasoning process.

2. Description of the Related Art

Generally, a semiconductor device is manufactured through a plurality ofunit processes comprising a deposition process for a thin film and anetching process. The etching process may be mostly performed withinsemiconductor device manufacturing equipment where the plasma reactionis induced. The semiconductor device manufacturing equipment iswet-cleaned according to preventive maintenance whenever it is used fora predetermined time for removing contaminants of polymer componentexcessively generated on an inner wall of the chamber due to the plasmareaction. Accordingly, it is required to perform the seasoning processto the chamber for stabilizing the plasma reaction right after thewet-cleaning.

SUMMARY

The present disclosure provides semiconductor device manufacturingequipment and a method of generating a seasoning process of the samecapable of increasing reproducibility of the seasoning process.

The present disclosure also provides semiconductor device manufacturingequipment and a method of generating a seasoning process of the samecapable of increasing or maximizing productivity.

In accordance with an example embodiment of the inventive concepts, amethod for seasoning semiconductor device manufacturing equipment mayinclude obtaining at least one reference measurement value according toa reference process recipe of a plasma reaction before cleaning achamber, selecting variables which affect the plasma reaction of thereference process recipe, obtaining test measurement values byperforming seasoning tests on the chamber, the test measurement valuesbeing associated with tests in which the selected variables aremanipulated, and estimating at least one estimated calculation valueapproximate to the at least one reference measurement value and aseasoning process recipe from a correlation of the manipulated variablesand the test measurement values.

In accordance with an example embodiment of the inventive concepts, amethod for seasoning semiconductor device manufacturing equipment mayinclude generating plasma in a reaction chamber using a productionprocess recipe, obtaining at least one reference measurement valuerelated to a byproduct of the generated plasma, performing a pluralityof seasoning tests on the chamber to obtain a plurality of test results,generating an empirical model by forming at least one relationalexpression correlating variables manipulated during the performing ofthe plurality of seasoning tests to the plurality of test results,estimating a seasoning process by using the at least one relationalexpression to calculate at least one estimated calculation value, andseasoning the reaction chamber using the seasoning process.

Example embodiments of the inventive concepts provide methods foroptimizing a seasoning process of a semiconductor device manufacturingequipment comprising obtaining reference measurement values according toa reference process recipe of a plasma reaction before cleaning achamber; selecting manipulated variables which affect a change of theplasma reaction from the reference process recipe; obtaining testmeasurement values according to a change of the manipulated variables byperforming seasoning tests of the chamber which induces the plasmareaction changing the manipulated variables after cleaning the chamber;and calculating at least one estimated calculation value approximate tothe reference measurement value and an optimum seasoning process recipefrom a correlation of the manipulated variables and the test measurementvalues.

In some embodiments, generating an empirical model according to thecorrelation of the manipulated variables and the test measurement valuesmay be further included.

In other embodiments, the number of the generated empirical model may beequal to that of the reference measurement values.

In still other embodiments, the empirical model may include theestimated calculation values according to a combination of themanipulated variables.

In even other embodiments, the estimated calculation values maycorrespond to a relational expression of the manipulated variables.

In yet other embodiments, the empirical model may include a square errorof the reference measurement values and the estimated calculationvalues.

In further embodiments, the optimum seasoning process recipe may becalculated in least-squares method by adding the square error andminimizing it.

In still further embodiments, the optimum seasoning process recipe maycorrespond to a vertex calculated from the square error of thesecond-order polynomial expression.

In even further embodiments, the vertex may be calculated by partialdifferentiating the square error of the second-order polynomialexpression for the manipulated variables or calculated from optimizationlogic.

In yet further embodiments, the optimum seasoning process recipe, in thecase that the vertex exists out of a constraint range, may correspond toa minimum manipulated variable within the constraint range.

In much further embodiments, the seasoning tests may be performedaccording to a regression analysis of the manipulated variables.

In still much further embodiments, the regression analysis may include adesign of experiment.

In even much further embodiments, the design of experiment may include aBox-Benken method.

In yet much further embodiments, the reference measurement values mayinclude at least one of optical measurement value and electricalmeasurement value of the plasma reaction whose difference between apatterned wafer and a bare wafer according to the reference processrecipe is large.

In yet still much further embodiments, the optical measurement value mayinclude a spectrum wavelength range of the plasma reaction.

In yet still much further embodiments, the optimum seasoning processrecipe may follow the same sequence as the reference process recipe.

In yet still much further embodiments, the optimum seasoning processrecipe may include a first oxide layer seasoning process for removing anatural oxide layer performed according to the reference process recipeof a trench forming process and a first silicon layer seasoning processfor removing a silicon layer.

In yet still much further embodiments, the optimum seasoning processrecipe may further include the first oxide layer seasoning process, atleast one oxide layer forming process sequentially performed followingthe first silicon layer seasoning process, a second oxide layerseasoning process, and a second silicon seasoning process.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the inventive concepts, and are incorporated in andconstitute a part of this specification. The drawings illustrate exampleembodiments of the inventive concepts and, together with thedescription, serve to explain principles of the inventive concepts. Inthe drawings:

FIG. 1 is a schematic diagram illustrating a semiconductor devicemanufacturing equipment according to an example embodiment of theinventive concepts;

FIG. 2 is a diagram illustrating a management system of thesemiconductor device manufacturing equipment illustrated in FIG. 1;

FIG. 3 is a flowchart illustrating a method of optimizing a seasoningprocess of the semiconductor device manufacturing equipment according tothe example embodiment of the inventive concepts;

FIG. 4 is a spectrum graph of a plasma reaction at a break through (BT)etching process;

FIG. 5 is a spectrum graph of a plasma reaction at a main etching (ME)process;

FIGS. 6A to 6D are diagrams illustrating spectrum graphs of a patternedwafer and a bare wafer corresponding to peak wavelength ranges ofranking 1 to ranking 4 of Table 1;

FIGS. 7A to 7D are diagrams illustrating spectrum graphs of a patternedwafer and a bare wafer corresponding to peak wavelength ranges ofranking 1 to ranking 4 of Table 2;

FIGS. 8A to 8D are diagrams illustrating spectrum graphs of a patternedwafer and a bare wafer corresponding to peak wavelength ranges ofranking 1 to ranking 4 of Table 3;

FIGS. 9A to 9C are diagrams illustrating spectrum graphs of a patternedwafer and a bare wafer corresponding to peak wavelength ranges ofranking 1 to ranking 6 of Table 4;

FIG. 10 is a diagram for explaining a Box-Benken method; and

FIG. 11 is a diagram illustrating an empirical model which continuouslyexpresses a relation of a test measurement value obtained through a testprocess performed according to a design of experiment using theBox-Benken method and manipulated test variables.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Example embodiments of the inventive concepts will be described below inmore detail with reference to the accompanying drawings. The inventiveconcepts may, however, be embodied in different forms and should not beconstrued as limited to the example embodiments set forth herein.Rather, the example embodiments are provided so that this disclosurewill be thorough and complete, and will fully convey the scope of theinventive concepts to those skilled in the art. Like reference numeralsrefer to like elements throughout.

The terms used in this disclosure are not for limiting the inventiveconcepts but for explaining the example embodiments. The terms of asingular form may include plural forms unless otherwise specified. Also,the meaning of “include,” “comprise,” “including,” or “comprising,”specifies a property, a region, a fixed number, a step, a process, anelement and/or a component but does not exclude other properties,regions, fixed numbers, steps, processes, elements and/or components.The reference numerals presented according to a sequence of explanationsare not limited to the sequence.

Hereinafter, example embodiments of the inventive concepts will bedescribed in detail with reference to the accompanying drawings.

FIG. 1 is a schematic diagram illustrating equipment 100 formanufacturing a semiconductor device in accordance with an exampleembodiment of the inventive concepts.

Referring to FIG. 1, the equipment 100 may include a control unit 60 forcontrolling first to third flow control units 32, 34, and 36. Thecontrol unit 60 may control different process recipes of a semiconductordevice production process and may also control a seasoning process thatgenerates byproducts of a plasma reaction after cleaning a chamber 10.In this example embodiment, the by products may be equally generated.The control unit 60 may obtain a production measurement value from firstto third plasma sensors 42, 44, and 46 during or after the semiconductordevice production process is performed according to a production processrecipe before cleaning the chamber 10. The semiconductor deviceproduction process is a unit process where a patterned wafer isfabricated within the chamber 10 and includes a reference process havingexcellent production yield. The production measurement value may includea reference measurement value. The seasoning process may include apreliminary process for making the inside of the chamber 10 suitable forperforming the semiconductor device production process after cleaningthe chamber 10. For the seasoning process, the plasma reaction of a barewafer may be performed within the chamber 10.

The control unit 60 may obtain a plurality of test measurement valuesfrom seasoning tests that are performed after the chamber 10 is cleaned.The control unit 60 may compute estimated calculation values accordingto a change of the test measurement values and produce a seasoning testprocess recipe for the first to third flow control unit 32 to 36 fromthe estimated calculation values. Accordingly, the control unit 60 mayselect the estimated calculation value approximate to the productionmeasurement value and produce an optimum seasoning process recipeaccording to the estimated calculation value.

The chamber 10 may provide inner space independent of the outside. Apump 50 may pump air into or out of the chamber 10. The chamber 10 mayinclude an etch chamber where a wafer or a thin layer on the wafer isetched by the plasma reaction. Within the etch chamber, an etch processmay be performed for patterning the wafer or at least one of a siliconlayer, an oxide layer, a nitride layer, and a metal layer on the wafer.The etch chamber may be connected to a transfer chamber buffering vacuumstate and a load-lock chamber as a cluster type.

First to a third gas supply units 22, 24, and 26 may supply a reactiongas and an inactive gas for etching the wafer or the thin layer into thechamber 10. For example, each of the first and second gas supply units22 and 24 may supply at least one reaction gas such as nitrogen (N₂),oxygen (O₂), chlorine (Cl₂), carbon tetrafluoride (CF₄), and bromic acid(HBr) to the chamber 10. The third gas supply unit 26 may supply theinactive gas such as argon to the chamber 10. The first to third flowcontrol units 32, 34, and 36 may include a valve for controlling a flowrate of the reaction gas and the inactive gas supplied into the chamber10. The third flow control unit 36 may be a pressure controller forcontrolling pressure in the chamber 10.

Preventive Maintenance (PM), e.g. wet-cleaning, for the chamber 10, maybe periodically performed every accumulated time of the semiconductordevice production process for removing a polymer component generated asbyproducts of the etch process. The polymer component may be depositedon an inner wall of the chamber 10 whenever the semiconductor deviceproduction process is performed. In the case that the polymer componentis piled up to more than a certain thickness, a lump of the polymercomponent may fall onto the wafer from the inner wall of the chamber 10and act as a particle contaminating a surface. For example, the PM forthe chamber 10 may be periodically performed every about 100 hours ofaccumulated use time.

Right after the PM of the chamber 10 is performed, etch characteristicsof a wafer or thin layer may be unstable. For example, after thewet-washing for the chamber 10, the plasma reaction may be unstable, oretch rate reproducibility of the wafer or thin layer may be degraded. Toovercome this problem, a seasoning process for the chamber 10 using abare wafer may be performed. The seasoning process may include apreliminary etch process for coating the inner wall of the chamber 10with the polymer component. The polymer component may affect the plasmareaction within the chamber 10.

First to a third plasma sensors 42, 44, and 46 may sense the plasmareaction in the chamber 10 optically or electrically. For example, thefirst plasma sensor 42 may include an Optical Emission Spectroscope(OES) for measuring light of the plasma reaction. The second plasmasensor 44 may include a Self-Excited Electron Resonance Spectroscope(SEERS). The third plasma sensor 46 may include a voltage-current (V-I)probe for probing a voltage and a current of the plasma reaction.

The control unit 60 may produce a seasoning process recipe for equallyimplementing optical or electrical characteristics of the plasmareaction during the semiconductor device production process at theseasoning process. The control unit 60 may obtain measurement values forthe plasma reaction in the chamber 10 from the first to third plasmasensors 42, 44, and 46.

As above-described, the control unit 60 may obtain a productionmeasurement value and an estimated calculation value of the plasmareaction at the semiconductor production process and the seasoning test.The control unit 60 may select an estimated seasoning process recipecorresponding to the estimated calculation value approximate to theproduction measurement value as the optimum seasoning process recipe.

Accordingly, since the semiconductor device manufacturing equipment 100according to the example embodiment of the inventive concepts includesthe control unit 60 for controlling the first to third flow controlunits 32, 34, and 36 for generating the byproducts of the plasmareaction of the semiconductor device production process in the samequantity also at the seasoning process, the reproducibility of theseasoning process of the chamber 10 may be improved. Also, since thecontrol unit 60 is capable of producing the optimum seasoning processrecipe, productivity and production yield may be maximized.

FIG. 2 is a diagram illustrating a management system of thesemiconductor device manufacturing equipment illustrated in FIG. 1. Ahost computer 200 may receive information of the seasoning processrecipe produced by the control unit 60 of the semiconductor devicemanufacturing equipment 100 and store it in a data base 210. Also, thehost computer 200 may output the seasoning process recipe information tothe control units 60 of the semiconductor device manufacturingequipments 100 required to perform the seasoning process for wholesemiconductor device production lines.

The host computer 200 may share or exchange the seasoning process recipeinformation with the control unit 60 of the semiconductor devicemanufacturing equipment 100 through a Semi Equipment CommunicationsStandard (SECS) protocol or a Transmission Control Protocol/InternetProtocol (TCP/IP).

Accordingly, the management system of the semiconductor devicemanufacturing equipment 100 according to the example embodiment of theinventive concepts may store the optimum seasoning process informationin the data base 210 and output it to the semiconductor devicemanufacturing equipment 100 through the host computer 200 if necessary.

As above, the semiconductor device manufacturing equipment 100 and itsmanagement system according to the example embodiments of the inventiveconcepts have been described referring to FIGS. 1 and 2. However, thisis just an exemplary explanation of the inventive concepts not meaningthat the inventive concepts are limited to the above-described equipmentand system. That is, the semiconductor device manufacturing equipment100 according to the inventive concepts may be applied to equipments formassively manufacturing semiconductor devices directly or in a modifiedform based on the above and below described inventive concepts.

A method for optimizing the seasoning process of the semiconductordevice manufacturing equipment 100 according to the example embodimentof the inventive concepts is described below in detail.

FIG. 3 is a flowchart illustrating the method of optimizing theseasoning process of the semiconductor device manufacturing equipment100 according to an example embodiment of the inventive concepts.

Referring to FIG. 3, a production measurement value is obtained from thestabilized plasma reaction in the chamber 10 during the semiconductordevice production process in operation S10. The production measurementvalue may include the optical and electrical characteristics of theplasma reaction induced in the chamber 10 during the semiconductordevice production process of the patterned wafer. The semiconductorproduction process may be performed according to an optimized productionprocess recipe. The production measurement value may be obtained fromthe first to third plasma sensors 42, 44, and 46 which measure theplasma reaction. The production measurement value may includemeasurement values of at least one of the first to third plasma sensors42, 44, and 46 where a relative difference of the plasma reaction of thepatterned wafer and the bare wafer is large in the chamber 10 where thesemiconductor device production process is performed. In detail, theproduction measurement value may be the measurement value of thepatterned wafer whose difference is large in comparison with the barewafer. The measurement value of the patterned wafer may be higher thanor lower than that of the bare wafer.

For instance, the semiconductor production process may include a processof forming a Shallow Trench Isolation (STI) trench of the wafer. Thetrench forming process may include a Break Through (BT) process forremoving a natural oxide layer on the surface of the wafer and a MainEtching (ME) process for forming the trench by removing crystallinesilicon in the wafer. Also, in the case that a depth of the trenchbecomes deep, the trench forming process may include an Oxidation (OX)process for forming an oxide layer by exposing the internal crystallinesilicon to oxygen after forming the trench to a predetermined depththrough the BT and ME processes and additionally performed BT and MEprocesses. The production measurement value may be selected from peaksof spectrum graphs illustrated in FIGS. 4 and 5 measured by the OESduring the BT and ME processes of the patterned wafer.

FIG. 4 is a spectrum graph of the plasma reaction at the BT process.FIG. 5 is a spectrum graph of the plasma reaction at the ME process.Herein, a horizontal axis denotes a spectrum wavelength of a visiblelight region, and a vertical axis denotes intensity of a spectrum peak.

Referring to FIGS. 4 and 5, the spectrum graphs at the BT and MEprocesses may have various peaks at the visible light region accordingto kinds of the reaction gas supplied into the chamber 10. The peak ofthe spectrum graph indicates optical unique characteristics of eachcorresponding element included in the reaction gas and may correspond toa particular wavelength range. In the spectrum graph, the wavelengthrange denoted by the peak may be called as a peak wavelength range foreasy understand.

The peak wavelength range may be not changed if the kinds of the wafersetched by the reaction gas are the same. During the BT and ME processesof the patterned wafer, the plasma reaction may generate the spectrum ofthe visible light region corresponding to the graphs of FIGS. 4 and 5.In the case of changing the wafer from the patterned wafer to the barewafer, the spectrum having the same peak wavelength range as the graphsof FIGS. 4 and 5 may be mostly measured. That is, in the case that thespectrum graphs of the plasma reaction at the etch processes of thepatterned wafer and the bare wafer are overlapped with each other, mostof the peak wavelength ranges may accord to each other. This is becausethe patterned wafer and the bare wafer may generate the byproducts ofthe plasma reaction of nearly same elements at the BT and ME processesrespectively.

Although the peak wavelength ranges of the patterned wafer and the barewafer may correspond with each other, the quantity of the byproducts ofthe plasma reaction at the etch processes of the patterned wafer and thebare wafer may be different. Thus, the patterned wafer and the barewafer may have different values of intensity at the spectrum graphcorresponding to the peak wavelength range. A gap between intensityvalues may be proportional to a difference between byproducts quantitiesof the plasma reaction corresponding to the corresponding peakwavelength range. This means that the difference between byproductsquantities of the plasma reaction is generated because subject materialsof the etching are different from each other. Accordingly, theproduction measurement value may include a spectrum intensity value ofthe patterned wafer which is largely different from the bare wafer atthe peak wavelength range of the spectrum. For instance, a ratio of thespectrum intensity values of the patterned wafer and the bare wafer atthe BT process may be ranked in percentage as shown in Tables 1 and 2.

TABLE 1 Patterned wafer/Bare Peak Corresponding elements ranking wafer ×100% wavelength of peak wavelength 1 539.8% 388.0 nm CN 2 425.0% 357.5nm NO, N₂, Cr, Zr 3 139.1% 791.0 nm 4 128.2% 337.0 nm CO₂, NH, O₂, Ti,O₂, N₂

TABLE 2 Bare wafer/Patterned Peak Corresponding elements ranking wafer ×100% wavelength of peak wavelength 1 139.3% 720.5 nm F 2 138.3% 641.5 nmGa, F, O₂, Ar 3 109.2% 703.0 nm Ar 4 108.2% 707.0 nm Ar

As shown in Table 1, the patterned wafer may generate the byproduct ofcarbon nitride (CN) corresponding to the peak wavelength range of about388.0 nm with about 539.8% intensity in comparison with the bare waferat the plasma reaction. Also, the patterned wafer may generate at leastone byproduct of nitrous oxide (NO), nitrogen (N₂), chrome (Cr), andzirconium (Zr) corresponding to the peak wavelength range of about 357.5nm with about 425.0% intensity in comparison with the bare wafer.Likewise, the patterned wafer may generate the byproduct correspondingto the peak wavelength ranges of about 791.0 nm and about 337.0 nm withabout 139.1% intensity and about 128.2% intensity in comparison with thebare wafer.

As shown in Table 2, the bare wafer may generate the byproduct offluorine (F) corresponding to the peak wavelength range of about 720.5nm with about 139.3% intensity in comparison with the patterned wafer atthe plasma reaction. Also, the bare wafer may generate at least onebyproduct of gallium, fluorine, oxygen, and argon corresponding to thepeak wavelength range of about 641.5 nm with about 138.3% intensity incomparison with the patterned wafer. Likewise, the bare wafer maygenerate the byproduct corresponding to the peak wavelength ranges ofabout 703.0 nm and about 707.0 nm with about 109.2% intensity and about108.2% intensity respectively in comparison with the patterned wafer.

Accordingly, the patterned wafer and the bare wafer may generate thebyproducts corresponding to the wavelength range where the spectrumintensity gap is large in different quantities at the BT process. Thepatterned wafer and the bare wafer may generate different quantities ofthe byproducts according to the rank orders shown in Tables 1 and 2. Theproduction measurement value may include the spectrum intensity value ofthe patterned wafer at the BT process. For instance, the productionmeasurement value may be selected from 8 wavelength ranges at the BTprocess.

FIGS. 6A to 6D are diagrams illustrating spectrum graphs of thepatterned wafer and the bare wafer corresponding to the peak wavelengthranges of the ranking 1 to ranking 4 of Table 1. Referring to FIGS. 6Ato 6D, it is shown that the difference of peak values of the ranking 1to ranking 4 of Table 1 is gradually decreased. The productionmeasurement value corresponding to the ranking 1 to ranking 4 is thepeak value of the patterned wafer and may include about 26, about 23,about 1.5, and about 1.6 at the corresponding peak wavelength range.

FIGS. 7A to 7D are diagrams illustrating spectrum graphs of thepatterned wafer and the bare wafer corresponding to the peak wavelengthranges of the ranking 1 to ranking 4 of Table 2. As shown in FIGS. 7A to7D, the production measurement value corresponding to the ranking 1 toranking 4 of Table 2 is the peak value of the patterned wafer and mayinclude about 2, about 1.7, about 2.4, and about 8 at the correspondingpeak wavelength range.

Accordingly, about 8 numbers of the production measurement value of theBT process may be obtained at the peak wavelength range where thedifference between the patterned wafer and the bare wafer from thespectrum of the plasma reaction is large.

A ratio of the spectrum intensity values of the patterned wafer and thebare wafer at the ME process may be ranked in percentage as shown inTables 3 and 4.

TABLE 3 Patterned wafer/bare wafer × Corresponding ranking 100% Peakwavelength elements 1 89.2% 465.5 nm AlO, CO, SiBr, Ar 2 85.4% 553.0 nm3 83.2% 460.0 nm CCl, CO, N, P 4 77.4% 256.5 nm CCl, Cl₂

TABLE 4 Bare wafer/Patterned Peak ranking wafer × 100% wavelengthCorresponding elements 1 43.0% 328.5 nm Ag, N₂ 2 42.8% 282.0 nm O₂, He,N₂, AsF, SiCl 3 40.5% 281.0 nm SiCl, OH, N₂ 4 34.0% 334.0 nm N2, Hg, Ti,GaCl, Ti, Zn 5 28.5% 243.5 nm Au, Si, As₂ 6 26.1% 336.0 nm NH, SiF

As shown in Table 3, the bare wafer may generate at least one byproductof aluminum oxide (AlO), carbon oxide (CO), silicon bromide (SiBr), andargon (Ar) corresponding to the peak wavelength range of about 465.5 nmwith about 89.2% intensity in comparison with the patterned wafer at theplasma reaction. Also, the bare wafer may generate the byproductcorresponding to the peak wavelength range of about 553.0 nm with about85.4% intensity in comparison with the patterned wafer. Likewise, thebare wafer may generate the byproduct corresponding to the peakwavelength ranges of about 460.0 nm and about 256.5 nm with about 83.2%intensity and about 77.4% intensity in comparison with the patternedwafer.

As shown in Table 4, the bare wafer may generate the byproduct such assilver (Ag) and nitrogen (N₂) corresponding to the peak wavelength rangeof about 328.5 nm with about 43.0% intensity in comparison with thepatterned wafer at the plasma reaction. Also, the bare wafer maygenerate at least one byproduct of oxygen (O₂), helium (He), nitrogen(N₂), arsenic fluoride (AsF), and silicon chloride (SiCl) correspondingto the peak wavelength range of about 282.0 nm with about 42.8%intensity in comparison with the patterned wafer. Likewise, the barewafer may generate the byproduct corresponding to the peak wavelengthranges of about 281.0 nm, about 334.0 nm, about 243.5 nm, and about336.0 nm with about 40.5% intensity, about 34.0% intensity, about 28.5%intensity, and about 26.1% intensity respectively in comparison with thepatterned wafer.

Accordingly, the patterned wafer and the bare wafer may generate thebyproducts corresponding to the wavelength range where the spectrumintensity gap is large in different quantities at the ME process. Thepatterned wafer and the bare wafer may generate different quantities ofthe byproducts according to the rank orders shown in Tables 3 and 4. Theproduction measurement value may include the spectrum intensity value ofthe patterned wafer at the ME process. For instance, the productionmeasurement value may be selected from 10 wavelength ranges at the MEprocess.

FIGS. 8A to 8D are diagrams illustrating spectrum graphs of thepatterned wafer and the bare wafer corresponding to the peak wavelengthranges of the ranking 1 to ranking 4 of Table 3. Referring to FIGS. 8Ato 8D, it is shown that the difference of peak values of the ranking 1to ranking 4 of Table 3 is gradually decreased. The productionmeasurement value corresponding to the ranking 1 to ranking 4 is thepeak value of the patterned wafer and may include about 1.8, about 2.3,about 1.9, and about 1.5 at the corresponding peak wavelength range.

FIGS. 9A to 9C are diagrams illustrating spectrum graphs of thepatterned wafer and the bare wafer corresponding to the peak wavelengthranges of the ranking 1 to ranking 6 of Table 4. As shown in FIGS. 9A to9C, the production measurement value corresponding to the ranking 1 toranking 6 of Table 4 is the peak value of the patterned wafer and mayinclude about 1.9, about 8.7, about 8.8, about 1.9, about 4, and about1.9 at the corresponding peak wavelength range.

Accordingly, about 10 numbers of the production measurement value of theME process may be obtained at the peak wavelength range where thedifference between the patterned wafer and the bare wafer from thespectrum of the plasma reaction is large.

Referring to FIG. 3 again, manipulated variables which greatly affectthe change of the plasma reaction are selected at the reference processrecipe in operation S20. The plasma reaction may be independentlygenerated in the inside of the chamber 10. The plasma reaction may bechanged mostly due to the kinds and flow rate of the reaction gassupplied into the chamber 10, pressure, and high frequency power. Thereaction gas may include material reactive to the patterned wafer.Accordingly, the reaction gas may greatly affect the opticalcharacteristics and electrical characteristics of the plasma reaction.The patterned wafer may be etched by 2 or more kinds of the reactiongases. For instance, the BT process of the patterned wafer may beperformed by the reaction gas comprising carbon tetrafluoride andnitrogen. Also, the ME process of the patterned wafer may be performedby the reaction gas comprising chlorine and bromic acid. The OX processof the patterned wafer may be performed by the reaction gas comprisingoxygen and the high frequency power inducing the plasma reaction.

Accordingly, the manipulated variables may include a control valuerelated to at least one reaction gas, the internal pressure of thechamber 10, and the high frequency power controlled by the first tothird flow control units 32, 34, and 36. For instance, the manipulatedvariables may include a flow rate value of a first reaction gas, a flowrate value of a second reaction gas, and a pressure value.

Next, the plurality of test measurement values is obtained by performingthe seasoning tests of the chamber 10 varying the manipulated variablesafter cleaning the chamber 10 in operation S30. The seasoning tests maybe performed by a regression analysis of the manipulated variables. Thenumber of performing the seasoning tests may be increased in proportionto the number of the manipulated variables. For instance, the seasoningtests may be performed with design of experiment. The design ofexperiment may minimize the number of the seasoning tests of the chamber10 according to variation of the manipulated variables. The design ofexperiment may include a factorial experiment design and an orthogonalexperiment design. The factorial experiment design may include aBox-Benken method which is a kind of a response surface method.

FIG. 10 is a diagram for explaining the Box-Benken method. TheBox-Benken method may include experiments corresponding to 12 dots atthe outside of a cube having a coordinate system where 3 manipulatedvariables are orthogonal to each other. Also, the Box-Benken method mayinclude 3 times of experiment corresponding to a single dot at aninternal center of the cube. Accordingly, the Box-Benken method mayinclude 15 times of experiment. The 15 times of seasoning test processaccording to the Box-Benken method for each of the BT and ME processesmay be performed as shown in Table 5.

TABLE 5 BT seasoning test process ME seasoning test process carbonBromic tetrafluoride Nitrogen Chlorine acid No. (CF₄) (N₂) Pressure(Cl₂) (HBr) Pressure 1 20 5 70 150 150 40 2 60 10 70 80 150 70 3 100 570 80 220 40 4 60 5 45 220 220 40 5 60 0 20 220 150 10 6 60 10 20 150220 70 7 20 10 45 150 150 40 8 20 0 45 150 80 70 9 60 5 45 80 80 40 10100 0 45 150 80 10 11 60 0 70 220 150 70 12 100 5 20 150 220 10 13 20 520 220 80 40 14 60 5 45 80 150 10 15 100 10 45 150 150 40

At the BT seasoning test process, the carbon tetrafluoride may becontrolled to have flow rates of about 20 SCCM, about 60 SCCM, and about100 SCCM. The nitrogen may be controlled to have flow rates of about 0SCCM, about 5 SCCM, and about 10 SCCM. The pressure may be adjusted toabout 20 mTorr, about 45 mTorr, and 70 mTorr. The supplied flow rates ofcarbon tetrafluoride and nitrogen and the pressure within the chamber 10may be varied based on the Box-Benken method having the combination ofTable 4. 4th, 9th, and 14th tests for the BT seasoning test process maybe performed supplying the carbon tetrafluoride and nitrogen with flowrates of about 60 SCCM and about 5 SCCM respectively under a pressure ofabout 45 mTorr.

At the ME seasoning test process, each of the chlorine and bromic acidmay be controlled to have flows rates of about 80 SCCM, about 150 SCCM,and about 220 SCCM, and the pressure may be adjusted to about 10 mTorr,about 40 mTorr, and about 70 mTorr. At the BT and ME seasoning testprocesses, the test measurement values may be obtained through at leastone of first to third measurers. For instance, the test measurementvalues may include the intensity value of the production measurementvalue at the spectrum wavelength range. 1st, 7th, and 15th tests for theME seasoning test process may be performed supplying the chlorine andbromic acid with flow rates of about 150 SCCM under a pressure of about40 mTorr.

Next, an empirical model is generated according to a correlation of thetest measurement value and the manipulated variables in operation S40.The empirical model may more continuously express the relation of thetest measurement values and the manipulated variables for each spectrumwavelength range. That is, the test measurement values may be expressedas a relational expression of the manipulated variables. For instance,the test measurement values may be expressed in a second-orderpolynomial expression corresponding to the relation of the manipulatedvariables as Equation (1).

$\begin{matrix}{{y_{1,1} = {{a_{1}x_{1}^{2}} + {b_{1}x_{2}^{2}} + {c_{1}x_{3}^{2}} + {d_{1}x_{1}x_{2}} + \mspace{259mu} {e_{1}x_{1}x_{3}} + {f_{1}x_{2}x_{3}} + {g_{1}x_{1}} + {h_{1}x_{2}} + {i_{1}x_{3}} + j_{1}}}{y_{1,2} = {{a_{1}x_{1}^{2}} + {b_{1}x_{2}^{2}} + {c_{1}x_{3}^{2}} + {d_{1}x_{1}x_{2}} + {e_{1}x_{1}x_{3}} + {f_{1}x_{2}x_{3}} + {g_{1}x_{1}} + {h_{1}x_{2}} + {i_{1}x_{3}} + j_{1}}}\vdots {y_{1,14} = {{a_{1}x_{1}^{2}} + {b_{1}x_{2}^{2}} + {c_{1}x_{3}^{2}} + {d_{1}x_{1}x_{2}} + {e_{1}x_{1}x_{3}} + {f_{1}x_{2}x_{3}} + {g_{1}x_{1}} + {h_{1}x_{2}} + {i_{1}x_{3}} + j_{1}}}{y_{1,15} = {{a_{1}x_{1}^{2}} + {b_{1}x_{2}^{2}} + {c_{1}x_{3}^{2}} + {d_{1}x_{1}x_{2}} + {e_{1}x_{1}x_{3}} + {f_{1}x_{2}x_{3}} + {g_{1}x_{1}} + {h_{1}x_{2}} + {i_{1}x_{3}} + j_{1}}}} & (1)\end{matrix}$

y_(1,1) to y_(1,15) may include the test measurement values at thespectrum wavelength range of the first ranking in 15 times of testexperiment based on the Box-Benken method. x₁ to x₃ are processconditions for obtaining the corresponding test measurement values andmay include values of the manipulated variables at the correspondingtest. a₁, b₁, c₁, d₁, e₁, f₁, g₁, h₁, i₁, j₁ are coefficients at thespectrum wavelength range of the first ranking. The coefficients may becalculated by 15 test measurement values and each value of themanipulated variables. Accordingly, the empirical model at the spectrumwavelength range of the first ranking may be generated as thesecond-order polynomial expression where the estimated calculation valuecorresponds to y₁. The empirical model may be generated as therespective second-order polynomial expressions corresponding to theestimated calculation values from y₁ to y₈ for the BT process. Also, theempirical model may be generated as the respective second-orderpolynomial expressions corresponding to the estimated calculation valuesfrom y₁ to y₁₀ for the ME process. That is, the empirical model maygenerate the respective second-order polynomial expressionscorresponding to 8 estimated calculation values of the first to fourthrankings of each of Tables 1 and 2 during the BT process. Also, theempirical model may generate the respective second-order polynomialexpressions corresponding to 10 estimated calculation values for therankings of Tables 3 and 4 during the ME process.

FIG. 11 is a diagram illustrating the empirical model which continuouslyexpresses the relation of the test measurement value obtained throughthe test process performed according to the design of experimentconfigured to the Box-Benken method and the manipulated variables.

Referring to FIG. 11, the empirical model may express combination of themanipulated variables as dots at the test experiments performedaccording to the Box-Benken method. Herein, the dots corresponding tothe combination of the manipulated variables should be expressed inthree-dimensions; however, they are expressed on only planes of threecoordinates in FIG. 11 because many dots may be overlapped with eachother making it difficult to distinguish them. The empirical model mayinclude the estimated values obtained from the combination of themanipulated variables.

Coordinate values of the estimated calculation values may correspond tothe estimated seasoning process recipe. The empirical model may expressthe estimated calculation values following the production measurementvalue as square error. If the square error is very large, the estimatedcalculation value may be expressed as a circle whose inside is empty.The estimated calculation value of small square error may be expressedas a circle dot which is gradually buried. If the square error issmaller than about 1 the estimated calculation value may be expressed asa black circle dot. If the square error is larger than about 1, theestimated calculation value may be expressed as a circle dot whoseinside is light. Rhombus dots express the test experiments of theBox-Benken method.

Accordingly, the empirical model may include the optimum combination ofmanipulated values corresponding to the estimated calculation value ofsmall square error at the peak wavelength range of the correspondingranking. Also, the empirical model may include the optimum estimatedseasoning process recipe for the plasma reaction at the spectrum of thecorresponding peak wavelength range. For instance, according to theoptimum estimated seasoning process recipe at the ME process, chlorineis about 160 SCCM, bromine is about 140 SCCM, and the pressure is about4 mTorr.

While the seasoning test process performed according to the Box-Benkenmethod expresses 3 manipulated variables in three dimensions, theempirical model may express in four dimensions comprising the 3manipulated variables and the estimated calculation values. Asabove-described, one empirical model may be generated for each spectrumwavelength range corresponding to the production measurement value. Ifthe number of the production measurement values is respectively 8 and 10at the BT and ME processes, 8 and 10 numbers of the empirical models mayalso be generated. In the empirical model, all the optimum combinationsof the manipulated variables for each spectrum wavelength range do notcoincide. This is because the estimated calculation values arestatistical values for finding the optimum combination of themanipulated variables from 8 empirical models at the BT process and 10empirical models at the ME process.

Referring to FIG. 3 again, finally, the optimum seasoning process recipeis produced from the empirical models in operation S50. The optimumseasoning process may be calculated in least-squares method by squaringthe errors of the production measurement values and the estimatedcalculation values and adding all of them. The least-squares method maybe expressed as Equation (2).

$\begin{matrix}{\sum\limits_{i = 1}^{n}{w_{i}\left( {{CV}_{i} - y_{i}} \right)}^{2}} & (2)\end{matrix}$

w_(i) is a weight coefficient of the ith ranking, CV_(i) is theproduction measurement value of the ith ranking, and y_(i) is theestimated calculation value of the ith ranking. w_(i) may be a randomlygiven constant according to importance of the ith ranking. If theestimated value is seen with the second-order polynomial expression ofEquation (1), the production measurement values, i.e., CV_(i) is aconstant, and Equation (2) may be fourth polynomial expression havingx₁, x₂, and x₃ as variables. 8 fourth polynomial expressions may becalculated at the BT process, and 8 fourth polynomial expressions may becalculated at the ME process. Each of the BT and ME processes may beexpressed as Equation (2) of fourth polynomial expression. Accordingly,values of x₁, x₂, and x₃ having minimum value of fourth polynomialexpression may be the optimum seasoning process recipe.

The minimum value of fourth polynomial expression may be a vertexapproximate to x₁, x₂, and x₃ axis. For instance, the vertex may becalculated from a solution of a cubic equation to which the fourthpolynomial expression is partial differentiated for x₁, x₂, and x₃.Also, the vertex may be calculated from optimization logic. In the casethat the vertex exists out of a constraint range of the manipulatedvariables, a minimum manipulated variable within the correspondingconstraint range may be selected as the optimum seasoning processrecipe. The constraint range may include a variation range allowed atthe semiconductor device production process. The optimum seasoningprocess recipe may be selected from each of the BT and ME processes.

Accordingly, since the optimum seasoning process recipe may becalculated based on the production measurement value of thesemiconductor device production process, the method of optimizing theseasoning process of the semiconductor device manufacturing equipment100 according to the example embodiment of the inventive concepts mayincrease the reproducibility of the seasoning process.

Further, the optimum seasoning process recipe may follow the samesequence as the production process recipe. In the chamber 10, if thetrench of the STI becomes deeper than a certain depth, the productionprocess comprising two times of silicon layer etch process may beperformed. Accordingly, the seasoning process may be performed twicelike the production process. For instance, for a process of forming thetrench of about 300 nm depth, after BT1 (removing first natural oxidelayer) process and ME1 (removing first silicon layer) process areperformed, the OX (plasma oxide layer formation) process may beperformed, and BT2 (removing second natural oxide layer) process and ME2(removing silicon layer) process may be sequentially performed.Likewise, the seasoning process of the chamber 10 for forming the trenchmay also be performed in order of the BT1, ME1, OX, BT2, and ME2processes. The optimum seasoning process recipe for them may becalculated as shown in Table 6.

TABLE 6 BT1 ME1 OX BT2 ME2 seasoning seasoning seasoning seasoningseasoning process process process process process recipe recipe reciperecipe recipe First flow  7 N₂ 162 Cl₂ 101 O₂   0 N₂ 174 Cl₂ controlunit Second 108 CF₄ 174 HBr 924 W 73.0 CF₄ 137 HBr flow control unitpressure  43 mT  65 mT  4.5 mT 26.5 mT  4.0 mT

For the BT1 seasoning process recipe, the supplying flow rate ofnitrogen and carbon tetrafluoride and the pressure may be calculatedhigher in comparison with the BT2 seasoning process. The BT1 and BT2processes may be performed by nitrogen and carbon tetrafluoride whoseflow rates are controlled by the first and second flow control units.For the BT1 seasoning process recipe, nitrogen is about 7 SCCM, carbontetrafluoride is about 108 SCCM, and the pressure is about 43 mTorr. Onthe contrary, for the BT2 seasoning process recipe, nitrogen is about 0SCCM, carbon tetrafluoride is about 73.0 SCCM, and the pressure is about26.5 mTorr. Accordingly, the BT1 seasoning process may be performed in astate of relatively high flow rate of the reaction gases and pressure incomparison with the BT2 seasoning process. Each of the BT1 and BT2 maybe performed for about 10 seconds.

The OX seasoning process may be performed right after the ME1 seasoningprocess is completed. For the OX seasoning process recipe, oxygen isabout 101 SCCM, the high frequency power is about 924 W, and thepressure is about 4.5 mTorr. The OX seasoning process may be performedfor about 10 seconds.

The ME1 and ME2 processes may be performed after the BT1 and BT2processes completed respectively. The ME1 and ME2 processes may beperformed by chlorine and bromic acid whose flow rates are controlled bythe first and second flow control units. For the ME1 and ME2 seasoningprocess recipes, the supplying flow rates of chlorine and bromic acidand the pressure may be differently calculated. For the ME1 seasoningprocess recipe, chlorine is about 162 SCCM, bromic acid is about 174SCCM, and the pressure is about 65 mTorr. For the ME1 seasoning processrecipe, chlorine is about 174 SCCM, bromic acid is about 137 SCCM, andthe pressure is about 4.0 mTorr. The ME1 seasoning process may beperformed in a state of mixed flow rates of the reaction gases andhigher pressure in comparison with the ME2 seasoning process. Each ofthe ME1 and ME2 seasoning processes may be performed for about 30seconds to about 2 minutes respectively.

Accordingly, the chamber 10 required to perform the etch process ofsequential 5 steps at the semiconductor device production process mayperform the seasoning process of 5 steps after the preventivemaintenance comprising the wet-cleaning. The chamber 10 may be convertedinto the semiconductor device production process right after 5 seasoningprocesses performed for a predetermined time. For instance, the chamber10 may perform the semiconductor device production process of thepatterned wafer after completing the seasoning process of about 4 hourwith the bare wafer.

As a result, since the seasoning process may be completed within shorttime by using the optimum seasoning process recipe calculated accordingto the measurement value of the semiconductor device production process,the method of optimizing the seasoning process of the semiconductordevice manufacturing equipment 100 according to the example embodimentof the inventive concepts may increase or maximize the productivity andproduction yield.

As above-described, according to the inventive concepts, since theoptimum seasoning process recipe can be calculated based on theproduction measurement of the semiconductor device production process,the reproducibility of the seasoning process can be increased.

Further, since the seasoning process can be completed within short timeby using the optimum seasoning process recipe, the productivity andproduction yield can be increased or maximized.

The above-disclosed subject matter is to be considered illustrative andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the inventive concepts. Thus, to themaximum extent allowed by law, the scope of the inventive concepts is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description.

1. A method for seasoning semiconductor device manufacturing equipment,comprising: obtaining at least one reference measurement value accordingto a reference process recipe of a plasma reaction before cleaning achamber; selecting variables which affect the plasma reaction of thereference process recipe; obtaining test measurement values byperforming seasoning tests on the chamber, the test measurement valuesbeing associated with tests in which the selected variables aremanipulated; and estimating at least one estimated calculation valueapproximate to the at least one reference measurement value and aseasoning process recipe from a correlation of the manipulated variablesand the test measurement values.
 2. The method of claim 1, furthercomprising: generating an empirical model according to the correlationof the manipulated variables and the test measurement values.
 3. Themethod of claim 2, wherein a number of the generated empirical models isequal to a number of the at least one reference measurement value. 4.The method of claim 2, wherein the empirical model generates the atleast one estimated calculation value according to a combination of themanipulated variables.
 5. The method of claim 4, wherein the at leastone estimated calculation value is generated using a relationalexpression of the manipulated variables.
 6. The method of claim 5,wherein the relational expression comprises a second-order polynomialexpression of the manipulated variables.
 7. The method of claim 6,wherein the empirical model comprises a square error of the at least onereference measurement value and the at least one estimated calculationvalue.
 8. The method of claim 7, wherein the seasoning process recipe isdetermined using a least-squares method by adding a square error andminimizing it.
 9. The method of claim 8, wherein the seasoning processrecipe corresponds to a vertex estimated from the square error of thesecond-order polynomial expression.
 10. The method of claim 1, whereinthe seasoning tests are performed according to a regression analysis ofthe manipulated variables.
 11. The method of claim 12, wherein theregression analysis comprises a design of experiment.
 12. The method ofclaim 13, wherein the design of experiment comprises a Box-Benkenmethod.
 13. The method of claim 1, wherein the at least one referencemeasurement value comprises at least one of an optical measurement valueand an electrical measurement value of the plasma reaction whosedifference between a patterned wafer and a bare wafer according to thereference process recipe is large.
 14. The method of claim 1, whereinthe seasoning process recipe follows a same sequence as the referenceprocess recipe.
 15. The method of claim 17, wherein the seasoningprocess recipe comprises a first oxide layer seasoning process forremoving a natural oxide layer performed according to the referenceprocess recipe of a trench forming process and a first silicon layerseasoning process for removing a silicon layer.
 16. The method of claim18, wherein the seasoning process recipe further comprises the firstoxide layer seasoning process, at least one oxide layer forming processsequentially performed following the first silicon layer seasoningprocess, a second oxide layer seasoning process, and a second siliconseasoning process.
 17. The method of claim 1, further comprising:seasoning the chamber according to the seasoning process recipe.
 18. Amethod for seasoning semiconductor device manufacturing equipment,comprising: generating plasma in a reaction chamber using a productionprocess recipe; obtaining at least one reference measurement valuerelated to a byproduct of the generated plasma; performing a pluralityof seasoning tests on the chamber to obtain a plurality of test results;generating an empirical model by forming at least one relationalexpression correlating variables manipulated during the performing ofthe plurality of seasoning tests to the plurality of test results;estimating a seasoning process by using the at least one relationalexpression to estimate at least one estimated calculation value; andseasoning the reaction chamber using the seasoning process.
 19. Themethod of claim 18, wherein the at least one relational expression is asecond-order polynomial.
 20. The method of claim 18, wherein theempirical model further includes a square error of the at least onereference measurement value and the at least one estimated calculationvalue.